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Personalized Content Recommendation Engine for Social Media Platform

senlainc.com

Telecommunications

Information technology

Media

Challenge

The client faces challenges in increasing user engagement, improving content discovery, and maximizing monetization potential. Current random content delivery is ineffective. They need a system to suggest relevant content based on individual user interests, improve user experience by offering personalized suggestions and discoveries, and narrow down user choices to enhance overall satisfaction and platform stickiness.

About the Client

A rapidly growing social media platform competing with established players, focused on enhancing user engagement and monetization through personalized content.

Objectives

Develop and deploy a personalized content recommendation engine to enhance user engagement.
Improve user experience through targeted and relevant content suggestions.
Increase user activity and time spent on the platform.
Enable targeted content discovery and expose users to a wider range of interests.
Drive monetization through personalized advertising and content promotion.

Functional Requirements

User Interest Profiling: Automatically infer user interests from activity data.
Content Recommendation: Suggest relevant content (posts, videos, articles, etc.) to users.
Creator Promotion: Connect content creators with relevant audiences.
Reporting & Analytics: Provide insights into recommendation performance and user engagement.
Content Categorization: Automatically categorize content to improve recommendation accuracy.
A/B Testing: Enable A/B testing of different recommendation algorithms.

Preferred Technologies

ELT Pipeline

Data Lake (e.g., AWS S3, Azure Data Lake Storage)

Recommendation Engine (Initially Amazon Personalize, with plan for custom model development)

Big Data Processing Framework (e.g., Spark, Flink)

Cloud Platform (e.g., AWS, Azure, GCP)

Integrations Required

User Authentication System
Content Management System
Advertising Platform
Analytics Platform

Non-Functional Requirements

Scalability: System must handle a large and growing user base and data volume.
Performance: Recommendations must be generated quickly and efficiently.
Security: Protect user data and prevent unauthorized access.
Reliability: The system should be highly available and fault-tolerant.
Data Privacy: Adhere to data privacy regulations (e.g., GDPR, CCPA).

Expected Business Impact

Successful implementation of this recommendation engine is expected to significantly increase user engagement (measured by time spent on the platform, content interactions), improve user retention, drive revenue through targeted advertising, and empower content creators. The project will enable the client to compete more effectively with larger social media platforms by providing a superior user experience.